Using AI Cameras to Measure Checkout Wait Times

09/10/2026

Identifying Bottlenecks from a Tense Meeting

On a Tuesday afternoon, the retail, IT, and operations teams gathered. The atmosphere was tense. The retail manager pointed to a spike in complaint metrics: "Customers are complaining about long lines, but we lack precise data to prove it." The IT manager countered: "We already have cameras, but they are only used for incident review. We cannot use them to measure wait times per transaction." The operations lead concluded: "If we don't know which shifts are overloaded, we can only increase staffing based on gut feeling, increasing labor costs without clear efficiency gains."

This is a familiar cycle for many retail businesses in Vietnam and neighboring markets like Thailand and the Philippines. Everyone agrees that data is needed, but no one knows where to start when the current camera system consists only of passive "black eyes."

Infrastructure Preparation and Defining Observation Zones

Inputs: Existing IP camera system (no hardware replacement required), store floor plan, list of checkout counters.
Tasks: Identify camera angles that cover the entire queuing area. With AIVISION's AI cameras, this step does not require physical changes, only software parameter adjustments. You need to clearly define the "waiting zone" and "service zone" on the configuration interface. Ensure sufficient lighting in this area for the computer vision algorithm to accurately detect people and objects.
Visible Output: A heatmap on the dashboard showing blind spots or overlapping observation zones.
Completion Sign: When looking at the map, you can clearly identify the start and end points of the queue for each counter, without confusion between customer flows.

Configuring Recognition and Counting Algorithms

Inputs: Access to the AIV Camera platform, administrator account.
Tasks: Activate the people and vehicle entry/exit counting feature. This sounds simple, but in reality, this is where the difference between a demo solution and a real deployment lies. You need to set the recognition confidence threshold. According to aivcamera.com, accuracy reaches 99%, but in real-world environments like crowded supermarkets, fine-tuning is mandatory. Test with real store data for 3-5 days before official operation.
Visible Output: Raw data logs recording when a person enters the waiting zone and when they exit the service zone.
Completion Sign: Randomly compare 20 customer instances in the video with the data logs. The deviation in wait time does not exceed 2-3 seconds.

Building a Wait Time Monitoring Dashboard

Inputs: Cleaned log data from the previous step.
Tasks: Create specific KPIs. Don't just look at "total wait time." Break it down into: Average wait time by time slot (morning, afternoon, evening), Average wait time by shift, and Wait time for customers with large carts (if the system allows classification). This is where AI monitoring delivers value. Instead of watching video continuously, you look at charts. AIVISION supports alerts via web, Telegram, or Zalo, helping managers receive information immediately when wait times exceed the set alert threshold (e.g., over 5 minutes).
Visible Output: An intuitive dashboard displaying colors based on overload levels (green, yellow, red).
Completion Sign: The retail manager can answer the question "Which counter is most overloaded at 11 AM?" within 5 seconds without asking the on-duty staff.

Training Staff Response Protocols

Inputs: Stable dashboard operation, incident handling procedures.
Tasks: This is the most easily overlooked step. Technology is just a tool. If staff do not know how to react when receiving an alert, all data is meaningless. Establish rules: When Counter A reports red, support staff in that area are responsible for directing customers to Counter B. When all counters report yellow, the shift needs to report to management to consider temporary staffing reinforcement or activating the express lane process.
Visible Output: Incident logs recording staff actions after receiving alerts.
Completion Sign: The rate of complaints about wait times drops to below 10% compared to pre-deployment levels. More importantly, staff proactively coordinate instead of waiting for customer complaints.

Evaluating Performance and Adjusting Staffing Strategy

Inputs: Operational data from the first 30 days.
Tasks: Analyze the correlation between wait times and sales. There is an interesting paradox: Sometimes, shorter wait times do not equate to the highest sales. Sometimes, customers accept longer waits to receive more thorough service. However, the main goal is to optimize labor costs. Data from AI cameras allows you to clearly see "dead" time slots where you are still maintaining full staffing. Try adjusting shift schedules based on real data rather than assumptions. For example, if data shows 80% of customers concentrate between 10 AM-12 PM and 3 PM-5 PM, allocate staff flexibly to these time slots.
Visible Output: A report comparing labor costs and customer satisfaction before and after implementation.
Completion Sign: You can prove that adjusting shifts based on data has helped reduce operational costs or increase revenue per employee work hour. This is no longer a matter of gut feeling, but an evidence-based decision.

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